RESUMO
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 µM to 2.3 µM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.
RESUMO
The microbatch technique is a simple and efficient method for screening for protein crystallization conditions both by hand and using automated systems. Many of the problems associated with more commonly used methods such as vapour diffusion can be overcome using this method. Despite its promise microbatch has not been widely utilized as a viable screening procedure. This review aims to describe the method from a practical point of view with an emphasis on screening crystallization conditions, outlining current progress and discussion of the advantages compared to other methods.
